scholarly journals A Quantum Ant Colony Multi-Objective Routing Algorithm in WSN and Its Application in a Manufacturing Environment

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3334 ◽  
Author(s):  
Fei Li ◽  
Min Liu ◽  
Gaowei Xu
Keyword(s):  
Simulation Analysis ◽  
Fitness Function ◽  
Routing Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Local Optimum ◽  
Manufacturing Environment ◽  
Multi Objective ◽  
Search Path ◽  
Manufacturing Environments

In many complex manufacturing environments, the running equipment must be monitored by Wireless Sensor Networks (WSNs), which not only requires WSNs to have long service lifetimes, but also to achieve rapid and high-quality transmission of equipment monitoring data to monitoring centers. Traditional routing algorithms in WSNs, such as Basic Ant-Based Routing (BABR) only require the single shortest path, and the BABR algorithm converges slowly, easily falling into a local optimum and leading to premature stagnation of the algorithm. A new WSN routing algorithm, named the Quantum Ant Colony Multi-Objective Routing (QACMOR) can be used for monitoring in such manufacturing environments by introducing quantum computation and a multi-objective fitness function into the routing research algorithm. Concretely, quantum bits are used to represent the node pheromone, and quantum gates are rotated to update the pheromone of the search path. The factors of energy consumption, transmission delay, and network load-balancing degree of the nodes in the search path act as fitness functions to determine the optimal path. Here, a simulation analysis and actual manufacturing environment verify the QACMOR’s improvement in performance.

Cutting trajectory planning of sections with complex composition for roadheader

2018 ◽  
Vol 233 (4) ◽  
pp. 1441-1452 ◽  
Author(s):  
Suyu Wang ◽  
Miao Wu
Keyword(s):  
Trajectory Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Grid Method ◽  
Ant Colony ◽  
Local Optimum ◽  
Complex Composition ◽  
Cutting Head ◽  
Pheromone Concentration ◽  
Simulation Results

In order to realize the autonomous cutting for tunneling robot, the method of cutting trajectory planning of sections with complex composition was proposed. Firstly, based on the multi-sensor parameters, the existence, the location, and size of the dirt band were determined. The roadway section environment was modeled by grid method. Secondly, according to the cutting process and tunneling cutting characteristics, the cutting trajectory ant colony algorithm was proposed. To ensure the operation safety and avoid the cutting head collision, the expanding operation was adopt for dirt band, and the aborting strategy for the ants trapped in the local optimum was put forward to strengthen the pheromone concentration of the found path. The simulation results showed that the proposed method can be used to plan the optimal cutting trajectory. The ant colony algorithm was used to search for the shortest path to avoid collision with the dirt band, and the S-path cutting was used for the left area to fulfill section forming by following complete cover principle. All the ants have found the optimal path within 50 times iteration of the algorithm, and the simulation results were better than particle swarm optimization and basic ant colony optimization.

Collaborative Manufacturing Resources Configuration Based on Summary Process Routes

2010 ◽  
Vol 97-101 ◽  
pp. 2707-2710
Author(s):  
Ying Ying Su ◽  
Jian Rong Wang ◽  
Wan Shan Wang
Keyword(s):  
Adaptive Control ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Configuration Model ◽  
Manufacturing Environment ◽  
Multi Objective ◽  
Collaborative Manufacturing ◽  
Manufacturing Resources ◽  
Improved Ant Colony Algorithm

Aiming at manufacturing resources configuration in collaborative manufacturing environment, configuration flow with process tasks decomposition based on improved ant colony algorithm was proposed. Process tasks were decomposed based on summary process routes of parts and a multi-objective configuration model to collaborative manufacturing resources configuration was built. Basic ant colony algorithm was improved for solving this model by the combination of adaptive control and pheromone update mechanism. Pheromone is in the range of a max-min interval based on ant colony algorithm with the maximal-minimal pheromone limit. Compared to basic ant colony algorithm, superiority of improved ant colony algorithm was revealed by simulation example

Cooperative Path Planning of Multiple UAVs by using Max–Min Ant Colony Optimization along with Cauchy Mutant Operator

2020 ◽  
pp. 2150002
Author(s):  
Zain Anwar Ali ◽  
Han Zhangang ◽  
Wang Bo Hang
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Fitness Function ◽  
Dynamic Environment ◽  
Global Optimum ◽  
Ant Colony ◽  
Local Optimum ◽  
Shortest Route ◽  
Optimum Position ◽  
Planning Problems

In a dynamic environment with wind forces and tornadoes, eliminating fluctuations and noise is critical to get the optimal results. Avoiding collision and simultaneous arrival of multiple unmanned aerial vehicles (multi-UAVs) is also a great problem. This paper addresses the cooperative path planning of multi-UAVs with in a dynamic environment. To deal with the aforementioned issues, we combine the maximum–minimum ant colony optimization (MMACO) and Cauchy Mutant (CM) operators to make a bio-inspired optimization algorithm. Our proposed algorithm eliminates the limitations of classical ant colony optimization (ACO) and MMACO, which has the issues of the slow convergence speed and a chance of falling into local optimum. This paper chooses the CM operator to enhance the MMACO algorithm by comparing and examining the varying tendency of fitness function of the local optimum position and the global optimum position when taking care of multi-UAVs path planning problems. It also makes sure that the algorithm picks the shortest route possible while avoiding collision. Additionally, the proposed method is more effective and efficient when compared to the classic MMACO. Finally, the simulation experiment results are performed under the dynamic environment containing wind forces and tornadoes.

scholarly journals Research on path planning of cleaning robot based on an improved ant colony algorithm

2021 ◽  
Vol 336 ◽  
pp. 07005
Author(s):  
Zhidong Wang ◽  
Changhong Wu ◽  
Jing Xu ◽  
Hongjie Ling
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Grid Method ◽  
Ant Colony ◽  
Local Optimum ◽  
Cleaning Robot ◽  
Initial Stage ◽  
Searching Ability ◽  
Improved Ant Colony Algorithm

The conventional ant colony algorithm is easy to fall into the local optimal in some complex environments, and the blindness in the initial stage of search leads to long searching time and slow convergence. In order to solve these problems, this paper proposes an improved ant colony algorithm and applies it to the path planning of cleaning robot. The algorithm model of the environmental map is established according to the grid method. And it built the obstacle matrix for the expansion and treatment of obstacles, so that the robot can avoid collision with obstacles as much as possible in the process of movement. The directional factor is introduced in the new heuristic function, and we can reduce the value of the inflection point of paths, enhance the algorithm precision, and avoid falling into the local optimal. The volatile factor of pheromones with an adaptive adjustment and the improved updating rule of pheromones can not only solve the problem that the algorithm falls into local optimum, but also accelerate the running efficiency of the algorithm in the later stage. Simulation results show that the algorithm has the better global searching ability, the convergence speed is obviously accelerated, and an optimal path can be planned in the complex environment.

scholarly journals An enhanced routing algorithm using ant colony optimization and VANET infrastructure

2019 ◽  
Vol 259 ◽  
pp. 02009 ◽  
Author(s):  
Noussaiba Melaouene ◽  
Rahal Romadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Road Network ◽  
Intelligent Transportation System ◽  
Routing Algorithm ◽  
Optimal Path ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Ant Colony ◽  
Specific Time

For the last fifty years, finding efficient vehicle routes has been studied as a representative logistics problem. In the transportation field, finding the shortest path in a road network is a common problem. VANET presents an innovation opportunity in the transportation field that enables services for intelligent transportation system (ITS) especially communication features. Because of VANET features [1] and despite road obstacles, a route for the shortest path can be established at a given moment. This paper proposes an enhanced algorithm, based on ACO Ant Colony Optimization and related to VANET infrastructure that aims to find the shortest path from the source to destination through the optimal path; in addition, a storage on static nodes is installed in each intersection in a VANET environment and for a specific time.

Global Path Planning for Mobile Robot Based on Improved Ant Colony Algorithms

2013 ◽  
Vol 418 ◽  
pp. 15-19 ◽  
Author(s):  
Min Huang ◽  
Ping Ding ◽  
Jiao Xue Huan
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Ant Colony ◽  
Mobile Robot Navigation ◽  
Local Optimum ◽  
Planning Problem ◽  
Optimal Path Planning ◽  
Global Path Planning ◽  
Global Optimal

Global optimal path planning is always an important issue in mobile robot navigation. To avoid the limitation of local optimum and accelerate the convergence of the algorithm, a new robot global optimal path planning method is proposed in the paper. It adopts a new transition probability function which combines with the angle factor function and visibility function, and at the same time, sets penalty function by a new pheromone updating model to improve the accuracy of the route searching. The results of computer emulating experiments prove that the method presented is correct and effective, and it is better than the genetic algorithm and traditional ant colony algorithm for global path planning problem.

An Evolutionary Mobility Aware Multi-Objective Hybrid Routing Algorithm for Heterogeneous WSNs

2017 ◽  
Vol 4 (3) ◽  
pp. 17-32 ◽  
Author(s):  
Nandkumar Prabhakar Kulkarni ◽  
Neeli Rashmi Prasad ◽  
Ramjee Prasad
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Routing Algorithm ◽  
Optimal Path ◽  
Wireless Sensor ◽  
Hybrid Routing ◽  
Multi Objective ◽  
Heterogeneous Wireless Sensor Networks ◽  
Hybrid Routing Protocol

Researchers have faced numerous challenges while designing WSNs and protocols in numerous applications. Amongst all sustaining connectivity and capitalizing on the network lifetime is a serious deliberation. To tackle these two problems, the authors have considered Mobile Wireless Sensor Networks (MWSNs). In this paper, the authors put forward an Evolutionary Mobility aware multi-objective hybrid Routing Protocol for heterogeneous wireless sensor networks (EMRP). EMRP selects the optimal path from source node to sink by means of various metrics such as Average Energy consumption, Control Overhead, Reaction Time, LQI, and HOP Count. The Performance of EMRP when equated with Simple Hybrid Routing Protocol (SHRP) and Dynamic Multi-Objective Routing Algorithm (DyMORA) using parameters such as Average Residual Energy (ARE), Delay and Normalized Routing Load. EMRP improves AES by a factor of 4.93% as related to SHRP and 5.15% as related to DyMORA. EMRP has a 6% lesser delay as compared with DyMORA.

Research on BP Neural Network Optimization Based on Ant Colony Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1819-1821 ◽  
Author(s):  
Rui Wang ◽  
Na Wang
Keyword(s):  
Ant Colony Algorithm ◽  
Learning Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Bp Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Initial Value ◽  
Neural Network Optimization ◽  
Optimal Weights

This paper presents an ant colony algorithm and BP algorithm together to complete the learning algorithm for neural networks ACO-BP algorithm. The algorithm adopts the ant colony algorithm for global optimization of the network weights, overcome the disadvantage of BP algorithm that is easy to fall into local optimum; then, the optimal weights found by BP algorithm as the initial value, further optimization. Finally, the simulation experiments show that, if the network structure is determined by the condition, this algorithm not only speeds up the convergence speed of the improved ant colony algorithm of optimal solution, but also can avoid falling into local optimal path. It will increase the reliability.

scholarly journals Multi-objective Chaotic Butterfly Optimization with Deep Neural Network based Sustainable Healthcare Management Systems

2021 ◽  
pp. 39-48
Author(s):  
Abedallah Zaid Abualkishik ◽  
◽  
◽  
Ali A. Alwan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Simulation Analysis ◽  
Fitness Function ◽  
Healthcare Services ◽  
Healthcare Systems ◽  
Healthcare Management ◽  
Management Systems ◽  
Multi Objective ◽  
Healthcare Data

Sustainable healthcare systems are developed to priorities healthcare services involving difficult decision-making processes. Besides, wearables, internet of things (IoT), and cloud computing (CC) concepts are involved in the design of sustainable healthcare systems. In this study, a new Multi-objective Chaotic Butterfly Optimization with Deep Neural Network (MOCBOA-DNN) is presented for sustainable healthcare management systems. The goal of the MOCBOA-DNN technique aims to cluster the healthcare IoT devices and diagnose the disease using the collected healthcare data. The MOCBOA technique is derived to perform clustering process and also to tune the hyperparameters of the DNN model. Primarily, the clustering of IoT healthcare devices takes place using a fitness function to select an optimal set of cluster heads (CHs) and organize clusters. Followed by, the collected healthcare data are sent to the cloud server for further processing. Furthermore, the DNN model is used to investigate the healthcare data and thereby determine the presence of disease or not. In order to ensure the betterment of the MOCBOA-DNN technique, an extensive simulation analysis take place. The experimental results portrayed the supremacy of the MOCBOA-DNN technique over the other existing techniques interms of diverse evaluation parameters.

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